Weather is notoriously difficult to predict. Models are subject to chaotic motion and must consider the initial conditions. The famous butterfly effect states that if a butterfly flaps its wings in Tahiti, that small event might cause a hurricane to hit Texas. This leads us to the following model: suppose that weather at time t is always between 0 and 1 and is governed by x_(t+1)=〖4x〗_t (1-x_t ). For x_0 = 0.2 and x_0 = 0.2000001, determine x_1; x_2;… x_50. Assume values for x closer to zero represent mild weather, and closer to 1 represent extreme weather. How do your calculations illustrate the butterfly effect? Support your answer with graphs.

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